In this study, a novel MAR method is developed to construct a 3D maxillofacial model from low-dose dental CBCT, where a key idea is to utilize explicit tooth shape prior information from intra-oral scan data whose acquisition does not require any extra radiation exposure. The proposed method comprises two core stages; (i) deep learning-based MAR and (ii) maxillofacial modeling. In the first stage, image-to-image deep learning is employed to mitigate metal-related artifacts from CBCT images, where metal-artifacted and images are used as input and ground-truth, respectively. To improve the learning ability, the proposed network is designed to take advantage of the intra-oral scan data as side-inputs and perform the multi-task learning of auxiliary tooth segmentation [1]}, [2]}. The suitable incorporation with explicit shape-prior of intra-oral scan data can bring significant positive effect in terms of learnability and feature extraction [3]}. In the second stage, a 3D maxillofacial model is constructed by segmenting the bones from the CBCT images corrected in the previous stage. For more accurate modeling, the weighted thresholding is applied, where the weight region is determined depending on the geometry of the intra-oral scan data.
